This paper uses the BERT model, which is a transformer-based architecture, to
solve task 4A, English Language, Sentiment Analysis in Twitter of SemEval2017.
BERT is a very powerful large language model for classification tasks when the
amount of training data is small. For this experiment, we have used the
BERT{\textsubscript{\tiny BASE}} model, which has 12 hidden layers. This model
provides better accuracy, precision, recall, and f1 score than the Naive Bayes
baseline model. It performs better in binary classification subtasks than the
multi-class classification subtasks. We also considered all kinds of ethical
issues during this experiment, as Twitter data contains personal and sensible
information. The dataset and code used in our experiment can be found in this
GitHub repository.

使用 BERT 模型对 SemEval2017 中 Twitter 上的英语情感分析任务 4A 进行解决，在训练数据量较小的分类任务中，BERT 是一个非常强大的大型语言模型。使用此模型进行实验时，我们使用了包含 12 个隐藏层的 BERT BASE 模型，该模型在准确性、精确率、召回率和 F1 分数上优于朴素贝叶斯基线模型，在二分类子任务中表现更好，我们还在实验过程中考虑了所有种类的伦理问题，因为 Twitter 数据包含个人和敏感信息。我们在此 GitHub 存储库中提供了实验中使用的数据集和代码。